Defining Cut Point for Kullback-Leibler Divergence to Detect Answer Copying

Authors : Arzu UÇAR, Celal DOĞAN

Pages : 156-166

Doi:10.21449/ijate.864078

View : 25 | Download : 9

Publication Date : 2021-03-15

Article Type : Research Paper

Abstract :Distance learning has become a popular phenomenon across the world during the COVID-19 pandemic. This led to answer copying behavior among individuals. The cut point of the Kullback-Leibler Divergence insert ignore into journalissuearticles values(KL); method, one of the copy detecting methods, was calculated using the Youden Index, Cost-Benefit, and Min Score p-value approaches. Using the cut point obtained, individuals were classified as a copier or not, and the KL method was examined for cases where the determination power of the KL method was 1000, and 3000 sample size, 40 test length, copiers` rate was 0.05 and 0.15, and copying percentage was 0.1, 0.3 and 0.6. As a result, when the cut point was obtained with the Min Score p-value approach, one of the cutting methods approaches, it was seen that the power of the KL index to detect copier was high under all conditions. Similarly, under all conditions, it was observed that the second method, in which the detection power of the KL method was high, was the Youden Index approach. When the sample size and the copiers` rate increased, it was observed that the power of the KL method decreased when the cut point with the cost-benefit approach was used.
Keywords : Copy detection, Cut point, ROC analysis, Answer copying, Kullback Leibler divergence